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Duoは、明確な所有権とプライバシーを確保しながら、AIの利便性を提供します。","ソフトウェア開発におけるAIの役割は今、極めて重要な局面を迎えています。今後、組織そしてDevSecOps担当者は、AIの効果的かつ責任ある使用を推奨するためにより積極的なアプローチを取る必要があります。\n\nそれと同時に、デベロッパーと幅広いDevSecOpsコミュニティは、AIに関する4つの世界的なトレンドに備えておく必要があります。それは、コードテストにおけるAI活用の拡大、知的財産（IP）およびプライバシーに対する継続的な脅威、AIバイアスの増加、そして、これらのリスクがあるにもかかわらずAI技術への依存が加速していることです。これらのトレンドに適切に対応することで、組織そしてDevSecOpsチームは成功を収めることができます。無視した場合、イノベーションが阻害され、最悪の場合、ビジネス戦略が頓挫してしまう可能性もあります。\n\n## 贅沢なことから標準へと変化：組織はAIを全面的に導入するように\nあらゆる業界の製品やサービスにおいて、AIを統合することは贅沢なことではなく、標準となり、DevSecOpsを活用し、AI搭載のソフトウェアによってAI機能を構築するようになるでしょう。AI主導の市場において競争力を保つためには、AIを活用してイノベーションを推進し、より優れた顧客価値を提供することが不可欠です。\n\nGitLabユーザーとのやり取りや業界トレンドのモニタリングの結果、組織はAIの導入によって効率性を最大化しようと取り組んでおり、2024年末までに3分の2以上の企業が自社の製品にAI機能を組み込む予定であることがわかりました。組織はAIで実験する段階からAI中心の段階へと変化しつつあります。\n\nこのような変化に備えるために、組織はソフトウェア開発のガバナンスの見直しに投資し、AI技術の継続的な学習と適応に注力する必要があります。これには文化面および戦略面での転換が必要となります。ビジネスプロセス、製品開発、顧客エンゲージメント戦略を見直す必要があります。そして、そのためにはトレーニングも必要です。DevSecOpsチームからは、トレーニングの希望と必要性について多くの声が寄せられています。当社の[グローバルDevSecOpsレポート](https://about.gitlab.com/ja-jp/developer-survey/)では、回答者の81%がAIの効果的な使用方法について、より多くのトレーニングを受けたいと答えています。\n\nAIがより洗練され、事業運営に不可欠になるにつれて、企業はAI主導のソリューションの倫理的意味や社会的な影響を考慮し、顧客やコミュニティに積極的に貢献できるようになる必要があります。\n\n## コードテストワークフローでのAIの利用が大幅に拡大\n\nDevSecOpsにおけるAIの進化は、すでにコードテストの方法に変革をもたらしつつあり、この傾向はさらに加速していくと考えられます。GitLabの調査によると、現在ソフトウェア開発の一環として自動テスト生成にAIを使用しているDevSecOpsチームは全体の41%に過ぎませんが、この数字は2024年末までに80%に達し、2年以内に100%近くになると予想されます。\n\n自社のワークフローにAIツールを統合する組織が増える中、各社はAIによってもたらされる効率性とスケーラビリティの向上に合わせて、現行のプロセスを調整するという課題に取り組んでいます。この変化により、生産性と精度が大幅に向上する一方で、従来のテストの役割と実践方法を大幅に調整する必要性も生じています。AI搭載のワークフローに適応するには、DevSecOpsチーム向けにAI管理に関するトレーニングを実施し、ソフトウェア製品の全体的な品質と信頼性が高まるよう、コードテストへの統合を促進するためにAIシステムを微調整する必要があります。\n\nさらに、このトレンドにより、品質管理担当者の役割が再定義され、AIベースのテストシステムを監督および強化するスキルを磨く必要性も生じます。AIシステムが高い効果を発揮するためには、継続的なモニタリングと指示が必要になるため、人間による監督は欠かせません。\n\n## ソフトウェアセキュリティにおいて知的財産（IP）やプライバシーにAIがもたらす脅威が加速\n\nAIを活用したコード作成の導入が進むにつれ、AIによって脆弱性がもたらされるリスクが増大し、ソフトウェアセキュリティ、企業の機密性、顧客データ保護に影響を及ぼすIPの漏洩やデータプライバシー侵害が幅広い範囲で発生する可能性が高まります。\n\nこうしたリスクを軽減するために、企業はAIの導入戦略において強固なIPおよびプライバシー保護を重視し、必ずAIの使用方法について完全な透明性を確保した上で導入する必要があります。AI関連のリスクを特定し、対処するには、厳格なデータガバナンスポリシーを導入し、高度な検出システムを実装することが極めて重要です。従業員へのトレーニングを通じてこういった問題に対する認識を高め、積極的なリスク管理文化を促すことは、IPおよびデータプライバシーを保護する上で不可欠です。\n\nまた、セキュリティ面でのAIの課題からは、今後もソフトウェア開発ライフサイクル全体を通じてDevSecOpsのプラクティスを実装し、最初から開発プロセスにおいてセキュリティとプライバシーを不可欠な部分と捉え、後回しにしないことが必要であることも示されています。つまり企業はAIの導入時に、DevSecOpsのシフトレフトの概念と同様、セキュリティを最優先して、セキュリティとプライバシーを犠牲にすることなく、AIを活用したイノベーションを実現できるようにする必要があります。\n\n## より良い時代が到来する前に増加するAIバイアスに備える\n\n2023年はAIが爆発的に普及した年でしたが、その台頭に伴い、アルゴリズムによるバイアスに注目が集まりました。AIツールはインターネット上のデータを利用してトレーニングを行うため、オンラインコンテンツにおいて表現されるさまざまなバイアスが反映されます。このような開発により、既存のバイアスを悪化させ、DevSecOpsにおけるAIの公平性と公正性に影響を及ぼす新たなバイアスを生み出すという、2つの課題がもたらされます。\n\nバイアスの蔓延に対抗するために、デベロッパーはトレーニング用データセットの多様化、公平性に関するメトリクスの採用、AIモデルへのバイアス検出ツールの導入に取り組むと同時に、特定のユースケース向けに設計されたAIモデルを検討する必要があります。検討すべきおすすめの方法のひとつは、AIが行うこと、行わないことについての確固たるガイドラインを定めた明確な一連の原則、つまり「憲法」に基づいて、AIフィードバックを使用してAIモデルを評価することです。AIによるバイアスのない出力を実現するためには、倫理的ガイドラインの確立とトレーニングへの介入が極めて重要です。\n\nAIシステムにおいて高品質なデータと信頼性を確保するためには、強固なデータガバナンスフレームワークを確立する必要があります。AIシステムは処理するデータの質によって左右されます。質の悪いデータがあると、不正確な出力や不適切な判断につながる可能性があります。\n\nデベロッパーと幅広い技術コミュニティは、バイアスの軽減を目的とした憲法AIや人間のフィードバックによる強化学習を通じて、バイアスのないAIの開発を求め、促進していくことが重要です。そのためには、AIプロバイダーとユーザー全体が協力して、公平性と透明性を優先した責任あるAI開発の実現に向け、取り組む必要があります。\n\n## DevSecOpsにおけるAI革命に備える\n組織によるAI中心のビジネスモデルへの移行が加速するにつれ、競争力を維持するためだけでなく、生き残るためにも移行が必要となっています。ビジネスリーダーとDevSecOpsチームは、プライバシーに対する脅威、AIの出力に対する信頼性、文化的抵抗の問題など、AIの利用によって増大すると想定される課題に対処する必要があります。\n\nまとめると、このようなAIの開発は、ソフトウェア開発とセキュリティの新時代の到来を表しています。こういった変化に適応するには、倫理的なAIの開発と使用、慎重なセキュリティおよびガバナンス対策、プライバシー保護への取り組みなど、包括的なアプローチが必要です。組織とDevSecOpsチームが現在どのように行動するかによって、DevSecOpsにおけるAIの長期的な未来に向けた道筋が決まり、安全で倫理的かつ効果的なデプロイを実現できます。\n\n_この記事は、2024年1月7日に[TechCrunch](https://techcrunch.com/2024/01/07/as-ai-becomes-standard-watch-for-these-4-devsecops-trends/)に掲載された記事の転載です。_\n","article","as-ai-becomes-standard-watch-for-these-4-devsecops-trends","content:ja-jp:the-source:ai:as-ai-becomes-standard-watch-for-these-4-devsecops-trends.yml","yaml","As Ai Becomes Standard Watch For These 4 Devsecops 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